Progesterone (P4) are aquatic contaminants that can impair fish reproduction even in low concentrations. The aim of this study was to investigate the effects of P4 on the sex differentiation, by quantitative determination of transcriptional changes of a candidate target gene (dax1, has a function in the sex determination and gonadal differentiation of several vertebrate species) in Misgurnus anguillicaudatus. We first cloned and characterized the full-length cDNAs for the dax1 in M. anguillicaudatus (designated as Ma-dax1). Sequence analysis reveals that Ma-dax1 shares high homology with dax1 in other species. Quantitative real-time PCR (qRT-PCR) and in situ hybridization showed that Ma-dax1 gene was highly conserved during vertebrate evolution and involved in a wide range of developmental processes including embryogenesis, central nervous system development and gonad development. For the P4 administration assay, groups of mature fish were exposed for 1, 7, 14, 21 and 28 days to nominal concentrations of 10, 100, and 1000 ng/L P4 in a flow-through system. Quantification of Ma-dax1 transcripts revealed the expression of Ma-dax1 mRNA is altered after P4 treatment in mature gonads. Those showed that P4 could influence the sexual development and sex differentiation in M. anguillicaudatus by disturbing sex differentiation-associated gene expression, and dax1 can be used as a sensitive molecular biomarker for early warning to monitor the environmental progestins chemicals in fresh water environment.
Background Among the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest. Results Here, we present a method that integrates gene ontology (GO) information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied to identify the genes that play a role in the cytotoxic responses of human hepatoblastoma cell line (HepG2) to saturated fatty acid (SFA) and tumor necrosis factor (TNF)-α, as compared to the non-toxic response to the unsaturated FFAs (UFA) and TNF-α. Incorporation of prior knowledge led to a better discrimination of the toxic phenotypes from the others. The model identified roles of lysosomal ATPases and adenylate cyclase (AC9) in the toxicity of palmitate. To validate the role of AC in palmitate-treated cells, we measured the intracellular levels of cyclic AMP (cAMP). The cAMP levels were found to be significantly reduced by palmitate treatment and not by the other FFAs, in accordance with the model selection of AC9. Conclusions A framework is presented that incorporates prior ontology information, which helped to (a) perform unsupervised clustering of the phenotypes, and (b) identify the genes relevant to each cluster of phenotypes. We demonstrate the proposed framework by applying it to identify physiologically-relevant feature genes that conferred differential toxicity to saturated vs. unsaturated FFAs. The framework can be applied to other problems to efficiently integrate ontology information and expression data in order to identify feature genes.
This paper focuses on the security of state estimation in supervisory control and data acquisition (SCADA) system in smart grids. The security index has been wildly studied to evaluate the vulnerability of SCADA systems under various types of cyber attacks, e.g., the false data injection (FDI) attack and distributed denial of service (DDoS) attack. However, it is shown that the calculation of security index is an NP-hard problem. Therefore, we propose an algebraic polynomial-time algorithm to calculate the security indices iteratively based on the orthogonal projection technique. Since existing modeling methods of security index only consider the attack overhead, without considering the impact of cyber attacks on the system performance, a novel hybrid security index is constructed. In the hybrid security index, the overheads of FDI attacks, DDoS attacks, and the impact of attack on the estimation error are considered simultaneously. Numerical results show that the protection of some critical measurements can enhance the overall security of SCADA systems.
Reconstructing gene networks from micro-array data can provide information on the mechanisms that govern cellular processes. Numerous studies have been devoted to addressing this problem. A popular method is to view the gene network as a Bayesian inference network, and to apply structure learning methods to determine the topology of the gene network. There are, however, several shortcomings with the Bayesian structure learning approach for reconstructing gene networks. They include high computational cost associated with analyzing a large number of genes and inefficiency in exploiting prior knowledge of co-regulation that could be derived from Gene Ontology (GO) information. In this paper, we present a knowledge driven matrix factorization (KMF) framework for reconstructing modular gene networks that addresses these shortcomings. In KMF, gene expression data is initially used to estimate the correlation matrix. The gene modules and the interactions among the modules are derived by factorizing the correlation matrix. The prior knowledge in GO is integrated into matrix factorization to help identify the gene modules. An alternating optimization algorithm is presented to efficiently find the solution. Experiments show that our algorithm performs significantly better in identifying gene modules than several state-of-the-art algorithms, and the interactions among the modules uncovered by our algorithm are proved to be biologically meaningful.
In temporal compressive imaging (TCI), high-speed object frames are reconstructed from measurements collected by a low-speed detector array to improve the system imaging speed. Compared with iterative algorithms, deep learning approaches utilize a trained network to reconstruct high-quality images in a short time. In this work, we study a 3D convolutional neural network for TCI reconstruction to make full use of the temporal and spatial correlation among consecutive object frames. Both simulated and experimental results demonstrate that our network can achieve better reconstruction quality with fewer number of layers.
As compressive imaging can capture high-resolution images using low-resolution detectors, it has received extensive attention recently. Compared to Single-pixel Compressive imaging, block compressive imaging (BCI) can considerably reduce the observation and calculation time of the reconstruction process, therefore it can also reduce the speed of imaging. A common challenge in BCI implementation is system calibration. In this paper, we use system spread point function into object reconstruction process to solve this challenge. In our simulation works, a 64x64 object with block size 4x4 is used. 6 measurements are collected for each block. Orthogonal matching pursuit (OMP) algorithm is applied to reconstruction. Additionally, we setup an experiment to demonstrate BCI idea. The BCI experimental platform confirms that images at high spatial resolution can be successfully recovered from low-resolution sensor.
In many situations, imagers are required to have higher imaging speed, such as gunpowder blasting analysis and observing high-speed biology phenomena. However, measuring high-speed video is a challenge to camera design, especially, in infrared spectrum. In this paper, we reconstruct a high-frame-rate video from compressive video measurements using temporal compressive imaging (TCI) with a temporal compression ratio T=8. This means that, 8 unique high-speed temporal frames will be obtained from a single compressive frame using a reconstruction algorithm. Equivalently, the video frame rates is increased by 8 times. Two methods, two-step iterative shrinkage/threshold (TwIST) algorithm and the Gaussian mixture model (GMM) method, are used for reconstruction. To reduce reconstruction time and memory usage, each frame of size 256×256 is divided into patches of size 8×8. The influence of different coded mask to reconstruction is discussed. The reconstruction qualities using TwIST and GMM are also compared.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)